PDF Structure Parsing for Long-Form Technical Questions
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Solution Overview
Problem
Conventional methods struggle to generate long-form technical questions from unstructured natural language documents, particularly from textbook documents, due to challenges in understanding context, ambiguity, and domain-specific language, limiting their effectiveness in educational assessment and tutoring systems.
Innovation Solution
A method and system for document structure-based unsupervised long-form technical question generation that utilizes Natural Language Processing (NLP) to extract metadata from PDF textbooks, annotate hierarchical index and Table of Content (TOC) structures, and instantiate predefined question templates to generate long-form questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods generate questions from unstructured natural language documents, then question generation is attempted, but the methods fail to generate long-form questions and struggle with context understanding
Solution Approach 1:
The patent segments the unstructured document into structured components by extracting hierarchical index structures and Table of Content structures. This segmentation transforms the raw text into organized sections with clear relationships, enabling reliable context understanding while maintaining question generation productivity.
Solution Approach 2:
The patent introduces document structure information (hierarchical index and TOC structures) as an intermediary between the unstructured natural language document and the question generation process. This intermediary provides contextual framework and organizational information that enables accurate long-form question generation without losing productivity.
2Productivity
If conventional methods use semantic templates for question generation, then some structure is considered, but long-form technical questions cannot be generated effectively
Solution Approach 1:
The patent performs preliminary action by extracting and organizing document structure information (hierarchical index and TOC structures) before the question generation process. This pre-processing creates ready-to-use structural frameworks that simplify the subsequent question generation, enabling long-form questions without increasing overall processing complexity.
3Adaptability or versatility
If one word or same idea is expressed using different words in natural language documents, then language flexibility is achieved, but ambiguity and context understanding become challenging
Solution Approach 1:
The patent uses document structure information as feedback to disambiguate words and expressions with multiple meanings. The hierarchical index and TOC structures provide contextual cues that help interpret ambiguous terms correctly, maintaining language flexibility while improving context interpretation accuracy through structural feedback.
Data Source
AI summary
The present disclosure a method for document structure based unsupervised long-form technical question generation. Initially, the system receives a textbook document. Further, a PDF metadata is extracted from the textbook document using a Natural Language Processing (NLP) technique. Further, a plurality of structures from the textbook document based on the PDF metadata using an NLP based filtering technique. Further, a plurality of index based question templates and Table of Contents (TOC) based question templates are obtained from a plurality of predefined question templates using the plurality of structures. Further, the generated plurality of long-form technical questions are generated using the obtained index and TOC based question templates. The plurality of long-form technical questions are further evaluated by the system using plurality of metrics. Further, the generated plurality of long-form technical questions are used to finetune a supervised question generation model for generating optimal questions from document structure.